The present invention relates to the field of image processing, and more particularly to a method and system for quantitatively characterizing the difference between reflectance spectra. Such characterization facilitates the performance of several useful image processing functions; including the quality evaluation of multispectral and hyperspectral imagery.
It has recently become possible to commercially obtain satellite and aerial images of terrain of interest from a number of sources. For example, certain large farms currently use satellite images provided by Landsat, the system of land-observing satellites operated by the federal government. Landsat satellites orbit the earth at approximately 900 km., and provide images in which each pixel represents a square area of between 1 m2 and 1E6 m2. A pixel area of 100 m2 is common for systems designed for land-use purposes. Visible, near-infrared, shortwave infrared, thermal infrared sensors deployed on such satellites can detect, among other things, the spectral reflectance, temperature, and other physical characteristics of specified terrestrial areas such as a farm""s fields. In one application, these images are overlaid onto farm mapping programs to show areas of plant stress or potential yield.
The sensors used in generating the images used for many commercial purposes are typically characterized as either xe2x80x9cmultispectralxe2x80x9d or xe2x80x9chyperspectralxe2x80x9d. Currently, multispectral sensors collect images of a terrain or landscape and provide a handful of wide spectral bands of imagery, which sample the visible, short wave infrared, and, in some instruments, thermal infrared portion of the electromagnetic spectrum. Similarly, hyperspectral sensors typically provide hundreds of narrow spectral bands of spatial imagery that span the visible, near-infrared, and shortwave infrared portion of the electromagnetic spectrum. As a result, images obtained using hyperspectral sensors generally afford greater spectral discrimination than those obtained using multispectral sensors.
Despite the existence of myriad techniques for processing image data collected from multispectral and hyperspectral sensors, there is not known to exist an objective standard for determining the quality of an image based upon its spectral characteristics. Conventionally, image quality is inferred based upon measurements of a number of parameters including, for example, spatial resolution, calibration accuracy, spectral resolution, signal to noise, contrast, bit error rate, dynamic range, sensor stability, and geometric registration. A manual and subjective image quality evaluation is known as the Multispectral Imagery Interpretability Rating Scale (xe2x80x9cMS IIRSxe2x80x9d). See, for example, the Multispectral Imagery Interpretability Rating Scale, Reference Guide (http://www.fas.org/irp/imint/niirs ms/msiirs.htm#IIRS), produced by the Image Resolution Assessment and Reporting Standards Committee (1995). However, the MS IIRS is currently continuing to be refined, and is not widely used. Attempts have also been made to derive mathematical constructs indicative of image quality. One such construct is known as The General Image Quality Equation (xe2x80x9cGIQExe2x80x9d) is used in parametric evaluation of single band images. See, e.g., Leachtenauer, J. C., Malila, W., Irvine J., Colburn, L., and Salvaggio, N., 10 Nov. 1997, General Image-Quality Equation, Applied Optics, Vol. 36, No. 32. The GIQE may also be used to produce an image quality value applicable to the National Interpretability Rating Scale (xe2x80x9cNIIRSxe2x80x9d). See, e.g., Civil NIIRS Reference Guide, Appendix III, History of NIIRS (http://www.fas.org/irp/imint/niirs c/), from the Image Resolution Assessment and Reporting Standards Committee (1996a) and the Civil NIIRS Reference Guide (http://www.fas.org/irp/imint/niirs c/guide.htm), also from the Image Resolution Assessment and Reporting Standards Committee (1996b). However, the MS IIRS, GIQE and NIIRS are not known to be useful in objectively assessing the quality of multispectral or hyperspectral images.
In summary, the present invention pertains to a method for measuring similarity between a first vector and a second vector wherein (i) each element of the first vector represents a first reflectance associated with a respective one of a plurality of spectral bands, and (ii) each element of the second vector represents a second reflectance associated with a respective one of such plurality of spectral bands. The inventive method contemplates determining both a magnitude difference and a shape difference between the first vector and the second vector. A similarity between the first vector and the second vector is computed based on such magnitude and shape differences.
In another aspect, the present invention relates to a method for measuring similarity between a first mean spectral vector and a second mean spectral vector. The inventive method contemplates deriving the first mean spectral vector from a first set of spectral vectors, and deriving the second mean spectral vector from a second set of spectral vectors. A magnitude difference and a shape difference are each determined between the first mean spectral vector and the second mean spectral vector. A similarity between the first mean spectral vector and the second mean spectral vector is computed based on the magnitude difference and the shape difference.
In yet another aspect, the present invention relates to an image processing method in which a first input pixel is extracted from a received spectral image. The first input pixel is converted into a first vector, wherein each element in the first vector represents a reflectance of a respective one of a plurality of spectral bands. A magnitude and a shape difference are determined between the first vector and a second vector. A similarity between the first vector and the second vector is determined based on these magnitude and shape differences.
The present invention also relates to an image processing system including an input interface through which is received a spectral image. The image processing system further includes a storage medium having stored therein a spectral similarity stored program. A processor of the image processing system is operative to execute the spectral similarity stored program and thereby: (i) organize pixels from the spectral image into a plurality of classes, (ii) determine a first mean reflectance vector for a first of said plurality of classes and a second mean reflectance vector for a second of said plurality of classes, and (iii) compute a similarity between said first mean reflectance vector and the second mean reflectance vector based upon a magnitude difference and a shape difference therebetween.